Adding Alexnet promise quantized source

llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/alexnet_promise_quant.cc

+
+#include <stdio.h> 
+#include <stdlib.h> 
+#include <unistd.h> 
+#include <fcntl.h> 
+#include <sys/types.h> 
+#include <sys/stat.h> 
+#include <string.h> 
+#include "../../../tensor_runtime/include/tensor_runtime.h" 
+#include "../../include/utils.h" 
+
+
+int main(){ 
+
+  llvm_hpvm_initTensorRt(1); 
+
+  int total_runs = 1; 
+  for (int i = 0 ; i < total_runs; i++){ 
+
+    startMemTracking(); 
+
+    int test_input_size = 5000; 
+    int batch_size = 5000;
+    int offset = 5000;
+    int batch_count = test_input_size / batch_size; 
+    float final_accuracy = 0.0; 
+
+    for(int i = 0; i < batch_count; i++){ 
+
+      std::string dir_prefix = std::string("../model_params/alexnet_cifar10_test/"); 
+      std::string input_path =  dir_prefix + std::string("input.bin"); 
+      std::string labels_path =  dir_prefix + std::string("labels.bin"); 
+      std::string conv2d_1_w_path =  dir_prefix + std::string("conv2d_1_w.bin"); 
+      void* conv2d_1_w =  readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,11,11); 
+      std::string conv2d_1_b_path =  dir_prefix + std::string("conv2d_1_b.bin"); 
+      void* conv2d_1_b =  readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1); 
+      std::string conv2d_2_w_path =  dir_prefix + std::string("conv2d_2_w.bin"); 
+      void* conv2d_2_w =  readTrainedWeights(conv2d_2_w_path.c_str(), 0,192,64,5,5); 
+      std::string conv2d_2_b_path =  dir_prefix + std::string("conv2d_2_b.bin"); 
+      void* conv2d_2_b =  readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,192,1,1); 
+      std::string conv2d_3_w_path =  dir_prefix + std::string("conv2d_3_w.bin"); 
+      void* conv2d_3_w =  readTrainedWeights(conv2d_3_w_path.c_str(), 0,384,192,3,3); 
+      std::string conv2d_3_b_path =  dir_prefix + std::string("conv2d_3_b.bin"); 
+      void* conv2d_3_b =  readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,384,1,1); 
+      std::string conv2d_4_w_path =  dir_prefix + std::string("conv2d_4_w.bin"); 
+      void* conv2d_4_w =  readTrainedWeights(conv2d_4_w_path.c_str(), 0,256,384,3,3); 
+      std::string conv2d_4_b_path =  dir_prefix + std::string("conv2d_4_b.bin"); 
+      void* conv2d_4_b =  readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,256,1,1); 
+      std::string conv2d_5_w_path =  dir_prefix + std::string("conv2d_5_w.bin"); 
+      void* conv2d_5_w =  readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,256,3,3); 
+      std::string conv2d_5_b_path =  dir_prefix + std::string("conv2d_5_b.bin"); 
+      void* conv2d_5_b =  readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1); 
+      std::string dense_1_w_path =  dir_prefix + std::string("dense_1_w.bin"); 
+      void* dense_1_w =  readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,4096,10); 
+      std::string dense_1_b_path =  dir_prefix + std::string("dense_1_b.bin"); 
+      void* dense_1_b =  readTrainedWeights(dense_1_b_path.c_str(), 0,1,10,1,1); 
+
+
+      int start = i * batch_size + offset; 
+      int end = (i + 1) * batch_size + offset; 
+
+      void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32); 
+
+      void* var_0 = ConvLayer_PROMISE(input, -1.8816426241908337, 2.0934095498544254, conv2d_1_w, -0.33087718, 0.3323643, conv2d_1_b, -0.7782218, 0.6020472, 5, 5, 1, 1, 0, 2, 0, -0.978641152381897, 0.9989452958106995, 9); 
+      void* var_1 = ConvLayer_PROMISE(var_0, -0.978641152381897, 0.9989452958106995, conv2d_2_w, -0.2095158, 0.33543423, conv2d_2_b, -0.45020863, 0.30596754, 2, 2, 1, 1, 0, 2, 0, -0.9997039437294006, 0.999930202960968, 9); 
+      void* var_2 = ConvLayer_PROMISE(var_1, -0.9997039437294006, 0.999930202960968, conv2d_3_w, -0.1715614, 0.17037082, conv2d_3_b, -0.6519161, 0.5939945, 1, 1, 1, 1, -1, 0, 0, -0.9999336004257202, 0.999940037727356, 9); 
+      void* var_3 = ConvLayer_PROMISE(var_2, -0.9999336004257202, 0.999940037727356, conv2d_4_w, -0.15575546, 0.14456555, conv2d_4_b, -0.55873865, 0.4704539, 1, 1, 1, 1, -1, 0, 0, -0.9999991059303284, 0.9999993443489075, 9); 
+      void* var_4 = ConvLayer_PROMISE(var_3, -0.9999991059303284, 0.9999993443489075, conv2d_5_w, -0.16108225, 0.16864482, conv2d_5_b, -0.22135437, 0.10401678, 1, 1, 1, 1, 0, 2, 0, -0.9994344115257263, 0.9996342062950134, 9); 
+      void* var_5 = FCLayer_PROMISE(var_4, -0.9994344115257263, 0.9996342062950134, dense_1_w, -0.18183032, 0.19018902, dense_1_b, -0.07189204, 0.106005594, -1, -15.076565380096437, 19.422585220336913, 9); 
+      void* var_6 = tensorSoftmax(var_5); 
+
+      uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end); 
+
+      float accuracy = computeAccuracy2(labels, batch_size, var_6); 
+      final_accuracy += accuracy; 
+      freeBatchMemory(); 
+ 
+    }
+
+    final_accuracy = final_accuracy / batch_count; 
+    dumpFinalAccuracy(final_accuracy); 
+  }
+
+  //dumpExecutionAccuracies(); 
+
+  llvm_hpvm_cleanupTensorRt(); 
+
+  return 0; 
+
+}